The Words Left Unsaid: New Study Shows Hidden Meaning Shapes Language Across Cultures

When someone says “the door was opened,” your mind instantly supplies a who. When a Malayalam speaker says “kadayil poyi”—literally “went to the store”—the person doing the going is nowhere in the sentence, yet every native listener knows exactly who is meant. This everyday act of mental completion, where absent elements carry real semantic weight, has long been treated as a footnote in linguistics. Now a team of researchers argues it deserves center stage.

In a study published in Frontiers in Language Sciences, Kiran Pala of the University of Eastern Finland, Vasudevan Nedumpozhimana of Trinity College Dublin, and colleagues at the Indian Institute of Technology Ropar introduce a framework they call “fulfillers”—semantic roles that are never spoken aloud but are grammatically, discursively, or culturally implied. Their proposal challenges the foundations of how computational systems and linguistic theories represent meaning, suggesting that what a sentence leaves out may matter as much as what it says.

Semantic role labeling, the process by which linguists and computer scientists assign structured meaning to words, has long relied on frameworks like PropBank and FrameNet. These systems excel at tagging explicit arguments: who did what to whom, where, and when. But they stumble when meaning depends on inference. In pro-drop languages like Hindi, Bangla, and Polish, subjects routinely vanish from sentences because verb endings make them recoverable. In topic-drop languages like Japanese, Korean, and Malayalam, even subjects and topics can disappear entirely, their identity carried by discourse context alone.

Traditional annotation schemes handle these gaps inconsistently at best. A classical system might mark an implicit subject if verb morphology makes it recoverable, but it has no mechanism for capturing the richer layers of meaning that listeners automatically reconstruct—the motivation behind an action, the emotional tone accompanying it, the cultural script that gives it sense. The researchers call this the difference between a bare event with an unfilled slot and the full, situated understanding a human listener actually experiences.

The fulfiller concept, which Pala first proposed in earlier work on cognitive semantics, goes beyond zero anaphora or null arguments. Those constructs are limited to syntactically licensed omissions—cases where grammar itself predicts what is missing. Fulfillers encompass something broader: inferences grounded in cultural scripts, affective states, and shared experiential knowledge. When an English speaker hears “she went to the store,” they don’t just recover the subject; they implicitly understand the routine of shopping, perhaps the mode of transport, the purpose of the errand. None of this is stated, yet all of it shapes interpretation.

To test whether fulfillers could be systematically identified rather than merely intuited, the team built a multilingual corpus of 194 sentences spanning English, Hindi, Malayalam, Bangla, and Bagri—a western Indo-Aryan language spoken in Rajasthan, Haryana, and Punjab. The corpus was deliberately asymmetric: English and Hindi were sampled at larger scale to support contrasts between idiomatic and pro-drop constructions, while the other languages served as targeted typological points of comparison. Two trained annotators, supervised by a semanticist, worked through the data using a layered annotation framework that distinguishes grammatical, discourse-pragmatic, and cultural-conceptual fulfillers.

The annotation process was rigorous but acknowledged its inherent subjectivity. Annotators first worked independently on a shared subset, resolving disagreements through discussion and achieving roughly 85 percent agreement on whether a fulfiller was present. Formal inter-annotator agreement metrics like Krippendorff’s alpha were deferred to a later phase, with the initial priority being guideline refinement rather than numerical scoring. The researchers also proposed complementary approaches—probabilistic annotation, confidence scores, and task-based evaluation—as ways to handle the framework’s inherently interpretive nature.

The corpus revealed a striking pattern. In pro-drop and topic-drop constructions, discourse-pragmatic fulfillers dominated: missing agents were recovered almost entirely through grammar and situational context, with culture playing little or no role. In idiomatic expressions, the picture reversed. Cultural and experiential fulfillers took over, drawing on shared knowledge that no amount of discourse tracking could supply. The Hindi idiom “Lakshman rekha langhana”—crossing the Lakshman line—derives its meaning not from verb agreement or context but from a mythological script rooted in the Ramayana. The Malayalam expression “kollam kandavanu illam venda”—one who has seen Kollam needs no home—relies on the historical salience of Kollam as a wealthy trading port.

This split has real consequences for how we understand linguistic typology. The existing literature treats referential density—how much of an argument a language leaves for context to supply—as a single property carried mainly by verb morphology. But the researchers found that the same language can behave as strongly context-dependent in everyday clauses while drawing on entirely different cultural resources in its idiomatic layer. A typology built only around the grammatical axis gets Hindi’s pro-drop behavior right and has nothing to say about its idioms. The two dimensions, grammatical and cultural, coexist within a single language rather than reducing to one parameter.

The implications extend beyond theory into practical applications. For machine translation, a fulfiller-annotated resource could flag expressions where literal translation loses cultural content—precisely the cases where current systems fail silently. The Bagri idiom “ganv-ganv gogo, ganv-ganv khejdi”—in every village the deity, in every village the khejri tree—means almost nothing to an English speaker, yet a literal rendering would score well on fluency metrics while losing nearly all its meaning. By marking fulfillers at the annotation stage, such failures become testable rather than invisible. The researchers acknowledge limitations: their corpus is regionally focused on Indo-Aryan and Dravidian languages, and they call for future work testing the framework on typologically distinct languages like Finnish and Turkish. But their core argument stands: absence is not a gap in language but a structured, inferable part of it—one that our models of meaning have been missing all along.

Subject of Research: A cognitive-semantic framework for annotating implicit, culturally and grammatically inferred meaning in multilingual language data.

Article Title: Semantic labeling with fulfillers in multilingual contexts

Article References: Pala, K., Nedumpozhimana, V., Shalu, S., Silu, P., & Choudhary, K. K. (2026). Semantic labeling with fulfillers in multilingual contexts. Frontiers in Language Sciences, 5, Article 1688982. https://doi.org/10.3389/flang.2026.1688982

Image Credits: AI Generated

DOI: 10.3389/flang.2026.1688982

Keywords: semantics, linguistic typology, pro-drop languages, idioms, semantic role labeling, cognitive semantics, multilingual corpora, machine translation, annotation framework, cross-linguistic variation, implicit meaning, natural language processing